Abstract: Autonomous systems increasingly operate alongside other strategic agents whose choices determine how well they perform. This talk presents game-theoretic tools for designing and analyzing such interactions, focusing on how information, coordination, and incentives shape outcomes in the presence of dynamics. I will first discuss dynamic Stackelberg games, in which a leader must act while updating its estimate of a strategic follower's intent in real time. By folding online estimates of the follower's objective into the leader's decision process, this framework captures the coupling between inference and control, and reveals that, unlike in optimal control, our standard notion of optimality is not consistent over time. I then turn to settings where independent decision-making is insufficient for achieving desirable outcomes, and show how correlated strategies offer a richer coordination mechanism, although it still needs to be reconciled with dynamical constraints. Finally, I discuss auction-based mechanisms for dynamical systems, where agents compete for limited resources under network constraints on their demands and actions.

Bio: Sarah H. Q. Li is an assistant professor in the School of Aerospace Engineering at the Georgia Institute of Technology, where she leads the Control, Coordination, and Competition under Uncertainty (C3U) Lab. She received her Ph.D. in aeronautics and astronautics at the University of Washington, and completed her postdoc with the Autonomous Control Lab at ETH Zurich. Her work combines optimal control, stochastic decision processes, and game theory to study how autonomous agents can coordinate safely and efficiently in uncertain environments.